December 11-2025
By: Marisa
HR professionals, let’s face the reality. Turnover is not just another statistic in a monthly report—it is a hidden cost that drains our time, energy, and training budget. Every time a high-performing employee leaves, we lose institutional knowledge, and the remaining team inevitably experiences lower morale and productivity.
We have long known that employees eventually leave. Our challenge now is to understand why they will leave, when they will leave, and most importantly, who they are. This is where Performance and Predictive Analytics become the most sophisticated “forecasting tools” we can possibly use.
Why We Can’t Rely Solely on Exit Interviews
Exit interviews are like autopsy results—they tell us why someone has already “died.”
What we need is a weather forecast, not a disaster report.
Predictive analytics allows us to shift from reactive firefighting to proactive prevention. We no longer wait for employees to submit resignation letters; instead, we use data to identify boiling points before employees burn out or become frustrated.
Practical Steps: Building a Predictive Turnover Model (Beyond Just Numbers)
As HR professionals, we don’t need to become data scientists—but we do need to know which data matters. Below are the three critical data pillars we must integrate to build a human-centered predictive model:
Pillar 1: Performance & Engagement Data (The “How”)
This is the soft data we often overlook because we focus too much on hard numbers.
Data Source | Indicators to Watch (Red Flags) |
Team Performance Data | Sudden drops in KPI scores or work quality without external explanation. |
Attendance & Leave Records | Sudden increase in emergency leave or unusual late arrivals. |
Communication & Collaboration Tools | Reduced interactions on team platforms (especially for WFH teams) or lower proactive contribution in meetings. |
Engagement/EVP Surveys | Consistent declines in specific areas (e.g., manager satisfaction, career opportunities). |
Human Touch Insight
Declining performance is often a symptom, not the root cause. This data tells us who is struggling so we can intervene with support—not judgment.
Pillar 2: Compensation & Promotion Data (The “What”)
This is hard data that indicates whether employees feel fairly valued.
- Internal Salary Ratio
Compare an employee’s salary with the internal average for the same role and experience level. Employees paid significantly below the average are at high risk. - Time Since Last Promotion/Pay Raise
How long does it take for a high-performing employee to receive advancement? Long delays signal stagnation risk. - External Market Analysis
Use market pay data. The wider the gap between your internal pay and market rates, the stronger the predictor of potential turnover.
Pillar 3: Training & Development Data (The “Growth Gap”)
Employees leave when they stop learning or can no longer see a future in the company.
- Training Completion Rates
Employees who suddenly stop completing mandatory training or upskilling courses may be losing motivation. - Career Path Reports
Employees who never receive meaningful career path discussions—or whose path remains unclear—are far more likely to seek clarity elsewhere.
Turning Data Into Immediate Action (The Intervention)
After your predictive model (even a simple 1–10 risk score from the three pillars) identifies “high-risk employees,” HR must act, not just report.
- Personalized, High-Touch Interventions
Do not send mass emails.
Invite the employee’s manager to conduct a Stay Interview, not an exit interview.
Focus the conversation on:
- “What makes you want to stay here?”
- “What can I change next week to make your work experience better?”
- Highly Segmented Solutions
If the data shows a Growth Gap → provide funding for external certifications.
If the issue is Burnout → enforce leave or adjust workload.
Solutions must be tailored to the root cause revealed by the data.
- Challenge the Managers
Use predictive risk scores as a new metric in manager performance reviews.
Managers whose teams show high turnover risk should receive intervention and coaching from HRBPs.
HR Is the Guardian of the Future
Using analytics to address turnover does not mean treating employees like robots; in fact, it means we care more deeply.
With predictive analytics, we can identify individuals who need support, guidance, and attention before it is too late. We shift from crisis managers to guardians of the organization’s future.
Let’s use the power of data to create workplaces where people don’t just stay, they grow.
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Together, let’s step toward a stronger Indonesia!
